Emergence and fragmentation of the alpha-band driven by neuronal network dynamics

Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the α-band and to the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the α-band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable α-band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing α and δ rhythms during anesthesia, we model the interaction of two excitatory networks with one inhibitory network, showing that this minimal topology underlies the generation of a persistent α-band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the α-band appears when external inputs are suppressed, while fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, α-oscillations could result from the synaptic dynamics of interacting excitatory neuronal networks with and without AHP, a principle that could apply to other rhythms.

Synaptic Plasticity and Oscillations in Alzheimer’s Disease: A Complex Picture of a Multifaceted Disease

Brain plasticity is widely accepted as the core neurophysiological basis of memory and is generally defined by activity-dependent changes in synaptic efficacy, such as long-term potentiation (LTP) and long-term depression (LTD). By using diverse induction protocols like high-frequency stimulation (HFS) or spike-timing dependent plasticity (STDP), such crucial cognition-relevant plastic processes are shown to be impaired in Alzheimer’s disease (AD). In AD, the severity of the cognitive impairment also correlates with the level of disruption of neuronal network dynamics. Currently under debate, the named amyloid hypothesis points to amyloid-beta peptide 1–42 (Aβ42) as the trigger of the functional deviations underlying cognitive impairment in AD. However, there are missing functional mechanistic data that comprehensively dissect the early subtle changes that lead to synaptic dysfunction and subsequent neuronal network collapse in AD. The convergence of the study of both, mechanisms underlying brain plasticity, and neuronal network dynamics, may represent the most efficient approach to address the early triggering and aberrant mechanisms underlying the progressive clinical cognitive impairment in AD. Here we comment on the emerging integrative roles of brain plasticity and network oscillations in AD research and on the future perspectives of research in this field.

Stochasticity Versus Determinacy in Neurobiology: From Ion Channels to the Question of the “Free Will”

If one accepts that decisions are made by the brain and that neuronal mechanisms obey deterministic physical laws, it is hard to deny what some brain researchers postulate, such as “We do not do what we want, but we want what we do” and “We should stop talking about freedom. Our actions are determined by physical laws.” This point of view has been substantially supported by spectacular neurophysiological experiments demonstrating action-related brain activity (readiness potentials, blood oxygen level–dependent signals) occurring up to several seconds before an individual becomes aware of his/her decision to perform the action. This report aims to counter the deterministic argument for the absence of free will by using experimental data, supplemented by computer simulations, to demonstrate that biological systems, specifically brain functions, are built on principle randomness, which is introduced already at the lowest level of neuronal information processing, the opening and closing of ion channels. Switching between open and closed states follows physiological laws but also makes use of randomness, which is apparently introduced by Brownian motion – principally unavoidable under all life-compatible conditions. Ion-channel stochasticity, manifested as noise, function is not smoothed out toward higher functional levels but can even be amplified by appropriate adjustment of the system’s non-linearities. Examples shall be given to illustrate how stochasticity can propagate from ion channels to single neuron action potentials to neuronal network dynamics to the interactions between different brain nuclei up to the control of autonomic functions. It is proposed that this intrinsic stochasticity helps to keep the brain in a flexible state to explore diverse alternatives as a prerequisite of free decision-making.

Critical and Ictal Phases in Simulated EEG Signals on a Small-World Network

Healthy brain function is marked by neuronal network dynamics at or near the critical phase, which separates regimes of instability and stasis. A failure to remain at this critical point can lead to neurological disorders such as epilepsy, which is associated with pathological synchronization of neuronal oscillations. Using full Hodgkin-Huxley (HH) simulations on a Small-World Network, we are able to generate synthetic electroencephalogram (EEG) signals with intervals corresponding to seizure (ictal) or non-seizure (interictal) states that can occur based on the hyperexcitability of the artificial neurons and the strength and topology of the synaptic connections between them. These interictal simulations can be further classified into scale-free critical phases and disjoint subcritical exponential phases. By changing the HH parameters, we can model seizures due to a variety of causes, including traumatic brain injury (TBI), congenital channelopathies, and idiopathic etiologies, as well as the effects of anticonvulsant drugs. The results of this work may be used to help identify parameters from actual patient EEG or electrocorticographic (ECoG) data associated with ictogenesis, as well as generating simulated data for training machine-learning seizure prediction algorithms.

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.

Increased molecular plasticity along the septotemporal axis of the hippocampus is associated with network functioning

Molecular plasticity crucially supports adaptive cellular and network functioning in the brain. Variations in molecular plasticity may yield important differences in neuronal network dynamics between discrete brain subregions. In the present study we show that the gradual development of sharp waves (SPWs), a spontaneous network activity that is organized under normalin vitroconditions in the CA1 field of ventral but not dorsal hippocampal slices, is associated with region selective molecular reorganization. In particular, increased levels of mRNAs for specific GABAAreceptor subunits (α1, β2, γ2) occurred in ventral hippocampal CA1 field during the development of SPWs. These mRNA changes were followed by a clear increase in GABAAreceptor number in ventral hippocampus, as shown by [3H]muscimol binding. An increase in mRNAs was also observed in dorsal slices for α2 and α5 subunits, not followed by quantitative GABAAreceptor changes. Furthermore, full development of SPWs in the CA1 field (at 3 hours of slice maintenancein vitro) was followed by increased expression of immediate early genes c-fos and zif-268 in ventral hippocampal slices (measured at 5 hoursin vitro). No change in c-fos and zif-268 levels is observed in the CA1 field of dorsal slices, which do not develop spontaneous activity. These results suggest that generation of SPWs could trigger specific molecular reorganization in the VH that may be related to the functional roles of SPWs. Correspondingly, the revealed increased potentiality of the ventral hippocampus for molecular reorganization may provide a clue to mechanisms that underlie the regulated emergence of SPWs along the longitudinal axis of the hippocampus. Furthermore, the present evidence suggests that dynamic tuning between spontaneous neuronal activity and molecular organization may importantly contribute to the functional segregation/heterogeneity seen along the hippocampus.